This document provides an overview of digital transformation and big data. It discusses key trends driving digital transformation like digitalization, social media, and mobility. It also covers what big data is, various sources of big data, how insights can be gained from big data analysis, and some of the ethical considerations around big data. The document outlines approaches for analyzing big data, including dealing with false correlations and overfitting models to vast amounts of data.
Why Big Data is the foundation for Digital Transformation ?Koray Sonmezsoy
ClickZ Live Hong Kong 4-6 Aug 2015
http://www.clickzlive.com/hongkong/agenda-day1.php
Digital transformation is top-of-mind for executives across many industries. Often when thinking of digital transformation, marketers are thinking about how to amplify their digital presence through website enhancements, mobile design and social platforms. While those are certainly key tactics in a robust digital strategy, they are initiatives that must be informed by and based on data.
How optimize the usage of data to driving innovation and efficiency, focused on Brazilian banking market landscape, highlighting main trends, key challenges, leverage managed data lakes and samples of use cases.
Big data is a phenomenon brought about by rapid data growth, complex, new, and changing data types, and parallel technology advancements; it brings huge possibilities. By optimizing these enormous amounts of structured and unstructured data, CSPs are in a unique position to capture these opportunities and create new revenue streams.
Big Data: Real-life examples of Business Value Generation with ClouderaCapgemini
Capgemini has helped multiple organizations to put Big Data to work and create value for their business and their clients.
This prsentation looks at real-world cases of how organizations are using, or planning to use, big data technology. It will look at the different ways in which the technology is being used in a business context.
Examples are drawn from Retail, Telco, Financial Services, Public Sector and Consumer goods.
It will look at a range of business scenarios from simple cost reduction through to new business models looking at how the business case has been built and what value has been realized.
It will also look at some of the practical challenges and approaches taken and specifically the application of Enterprise Data Hubs in collaboration with its prime partner Cloudera.
Written by Richard Brown, Global Programme Leader, Big Data & Analytics, Capgemini
Digital Transformation: How to Build an Analytics-Driven CultureAlexander Loth
http://alexloth.com/2017/12/11/diversify-long-term-crypto-portfolio/
<- Follow-up blog post "How to diversify a Long-term Crypto Portfolio"!
Executive Talk, Frankfurt School of Finance & Management, 8 December 2017
Why Big Data is the foundation for Digital Transformation ?Koray Sonmezsoy
ClickZ Live Hong Kong 4-6 Aug 2015
http://www.clickzlive.com/hongkong/agenda-day1.php
Digital transformation is top-of-mind for executives across many industries. Often when thinking of digital transformation, marketers are thinking about how to amplify their digital presence through website enhancements, mobile design and social platforms. While those are certainly key tactics in a robust digital strategy, they are initiatives that must be informed by and based on data.
How optimize the usage of data to driving innovation and efficiency, focused on Brazilian banking market landscape, highlighting main trends, key challenges, leverage managed data lakes and samples of use cases.
Big data is a phenomenon brought about by rapid data growth, complex, new, and changing data types, and parallel technology advancements; it brings huge possibilities. By optimizing these enormous amounts of structured and unstructured data, CSPs are in a unique position to capture these opportunities and create new revenue streams.
Big Data: Real-life examples of Business Value Generation with ClouderaCapgemini
Capgemini has helped multiple organizations to put Big Data to work and create value for their business and their clients.
This prsentation looks at real-world cases of how organizations are using, or planning to use, big data technology. It will look at the different ways in which the technology is being used in a business context.
Examples are drawn from Retail, Telco, Financial Services, Public Sector and Consumer goods.
It will look at a range of business scenarios from simple cost reduction through to new business models looking at how the business case has been built and what value has been realized.
It will also look at some of the practical challenges and approaches taken and specifically the application of Enterprise Data Hubs in collaboration with its prime partner Cloudera.
Written by Richard Brown, Global Programme Leader, Big Data & Analytics, Capgemini
Digital Transformation: How to Build an Analytics-Driven CultureAlexander Loth
http://alexloth.com/2017/12/11/diversify-long-term-crypto-portfolio/
<- Follow-up blog post "How to diversify a Long-term Crypto Portfolio"!
Executive Talk, Frankfurt School of Finance & Management, 8 December 2017
On behalf of SBI Consulting I’ve made a webinar on September 25th about Data Monetization.
In the post covid-19 era, transformation of businesses to govern their data more as an asset will become of huge importance. Becoming more data driven and digital will only increase at an unseen pace.
The essence of this transformation and the emphasis will be on Data Monetization. Monetizing your data assets will be of vital importance if you’d want to remain competitive and survive & thrive in the new normal.
In this webinar “Data Monetization in a post-Covid era”, I cover topics such as:
What does Data Monetization entails
Why Data Monetization is important for your business
How does the post-Covid era impacts this monetization process
What do we mean with Infonomics and Data Debt
The 5 key takeaways to get started with Data Monetization
The outcome? A good understanding of Data Monetization and practical insights to get going immediately!
Financial Markets Data & Analytics Led TransformationGianpaolo Zampol
How big data, advanced analytics and cognitive computing is disrupting traditional business and operating models in financial markets? New competitors, powered by social, mobile, analytics, and cloud computing, are making new business models emerging rapidly. Wealth Management, Corporate Banking and Transaction Banking & Payments are significant sources of growth in Financial Markets. How take advantage from those new technologies to face this new scenario?
Mohanbir Sawhney, Robert R. McCormick Tribune Foundation Clinical Professor of Technology Kellogg School of Management, Northwestern University presents at the 2012 Big Analytics Roadshow.
Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage.... read more: http://www.biganalytics2012.com/sessions.html#mohan_sawhney
Nos données face à l'incertain: la culture data par Benjamin Protais (Busi...Marco Brienza
Benjamin Protais présente les clés pour qu'une entreprise devienne agile par l'intermédiaire de ses données. Et qu'elle le reste! L'incertitude a augmenté le risque et le coût associé à une donnée de mauvaise qualité. Comment orienter ses équipes vers une "culture data" dans un contexte économique et légal en constant mouvement ?
Analytics driving innovation and efficiency in BankingGianpaolo Zampol
Point of view around main trends and challenges to leverage Analytics in Banking industry, looking for Brazilian market landscape.
Overview on key and emerging topics: Big Data & Analytics, Fundamental Review of Trading Book (FRTB) and Risk-Adjusted Performance Management (RAPM)
Big Data: Real-life Examples of Business Value GenerationCapgemini
This presentation looks at real-world cases of how organizations are using, or planning to use, big data technology to drive value. It looks at the different ways in which the technology is being used in a business context. Examples are drawn from Retail, Telco, Financial Services and Consumer goods.
It also develops a range of business scenarios from simple cost reduction through to new business models specifically looking at how the business case has been built and what value has been realized.
First presented by Richard Brown, Capgemini Program Lead for Business Information Management, at the IP Expo – Big Data Summit 2014.
http://www.capgemini.com/big-data-analytics
Open Innovation - Winter 2014 - Socrata, Inc.Socrata
As innovators around the world push the open data movement forward, Socrata features their stories, successes, advice, and ideas in our quarterly magazine, “Open Innovation.”
The Winter 2014 issue of Open Innovation is out. This special year-in-review edition contains stories about some of the biggest open data achievements in 2013, as well as expert insights into how open data can grow and where it may go in 2014.
Strategy how to change Big Data into useful information and win the business/candidacy, and Big Problem into Big Opportunity in the information exposure era.
To maintain relevance in the digital age, Accounting Firms must undergo a digital transformation – an evolution the firm must go through to add digital dimensions to not only client experiences but for the firm itself.
On behalf of SBI Consulting I’ve made a webinar on September 25th about Data Monetization.
In the post covid-19 era, transformation of businesses to govern their data more as an asset will become of huge importance. Becoming more data driven and digital will only increase at an unseen pace.
The essence of this transformation and the emphasis will be on Data Monetization. Monetizing your data assets will be of vital importance if you’d want to remain competitive and survive & thrive in the new normal.
In this webinar “Data Monetization in a post-Covid era”, I cover topics such as:
What does Data Monetization entails
Why Data Monetization is important for your business
How does the post-Covid era impacts this monetization process
What do we mean with Infonomics and Data Debt
The 5 key takeaways to get started with Data Monetization
The outcome? A good understanding of Data Monetization and practical insights to get going immediately!
Financial Markets Data & Analytics Led TransformationGianpaolo Zampol
How big data, advanced analytics and cognitive computing is disrupting traditional business and operating models in financial markets? New competitors, powered by social, mobile, analytics, and cloud computing, are making new business models emerging rapidly. Wealth Management, Corporate Banking and Transaction Banking & Payments are significant sources of growth in Financial Markets. How take advantage from those new technologies to face this new scenario?
Mohanbir Sawhney, Robert R. McCormick Tribune Foundation Clinical Professor of Technology Kellogg School of Management, Northwestern University presents at the 2012 Big Analytics Roadshow.
Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage.... read more: http://www.biganalytics2012.com/sessions.html#mohan_sawhney
Nos données face à l'incertain: la culture data par Benjamin Protais (Busi...Marco Brienza
Benjamin Protais présente les clés pour qu'une entreprise devienne agile par l'intermédiaire de ses données. Et qu'elle le reste! L'incertitude a augmenté le risque et le coût associé à une donnée de mauvaise qualité. Comment orienter ses équipes vers une "culture data" dans un contexte économique et légal en constant mouvement ?
Analytics driving innovation and efficiency in BankingGianpaolo Zampol
Point of view around main trends and challenges to leverage Analytics in Banking industry, looking for Brazilian market landscape.
Overview on key and emerging topics: Big Data & Analytics, Fundamental Review of Trading Book (FRTB) and Risk-Adjusted Performance Management (RAPM)
Big Data: Real-life Examples of Business Value GenerationCapgemini
This presentation looks at real-world cases of how organizations are using, or planning to use, big data technology to drive value. It looks at the different ways in which the technology is being used in a business context. Examples are drawn from Retail, Telco, Financial Services and Consumer goods.
It also develops a range of business scenarios from simple cost reduction through to new business models specifically looking at how the business case has been built and what value has been realized.
First presented by Richard Brown, Capgemini Program Lead for Business Information Management, at the IP Expo – Big Data Summit 2014.
http://www.capgemini.com/big-data-analytics
Open Innovation - Winter 2014 - Socrata, Inc.Socrata
As innovators around the world push the open data movement forward, Socrata features their stories, successes, advice, and ideas in our quarterly magazine, “Open Innovation.”
The Winter 2014 issue of Open Innovation is out. This special year-in-review edition contains stories about some of the biggest open data achievements in 2013, as well as expert insights into how open data can grow and where it may go in 2014.
Strategy how to change Big Data into useful information and win the business/candidacy, and Big Problem into Big Opportunity in the information exposure era.
To maintain relevance in the digital age, Accounting Firms must undergo a digital transformation – an evolution the firm must go through to add digital dimensions to not only client experiences but for the firm itself.
Digital Transformation How to Reboot IT and Business CollaborationBizagi
70% of organizations say that efforts to transform the business are undermined by internal complexity, including legacy technologies and a lack of collaboration between the business and IT.
Business functions can’t wait months for solutions, but IT leaders must to retain oversight to prevent digital projects from spiralling out of control.
View this presentation from a live Webinar to see how Takeda Pharmaceuticals has used a Digital Business Platform to rapidly build agile applications approved by IT, but owned and customized by the business teams that use them – unlocking benefits that would be attractive to any organization.
View to get practical insights from how Takeda:
•Enabled the operational agility needed to digitally transform
•Rapidly digitized core processes including procurement
•Unlocked the potential for enterprise-wide cost savings
Transforming Business in a Digital Era with Big Data and MicrosoftPerficient, Inc.
The socially integrated world, the rise of mobile, the Internet of Things - this explosion of data can be directed and used, rather than simply managed. That's why Big Data and advanced analytics are key components of most digital transformation strategies.
In the last year, Microsoft has made key moves to extend its data platform into this realm. Stalwart platforms like SQL Server and Excel join up with new PaaS offerings to make up a dynamic and powerful Big Data/advanced analytics ecosystem.
In this webinar, our experts covered:
-Why you should include Big Data and advanced analytics in your digital transformation strategy
-Challenges facing digital transformation initiatives
-What options the Microsoft toolset offers for Big Data (Hadoop) and advanced analytics
-How to leverage products and services you already own for your digital transformation
Thinking Small: Bringing the Power of Big Data to the MassesFlutterbyBarb
Thinking Small: Bringing the Power of Big Data to the Masses via Adobe with the results of improved access to insights, better user experiences, and greater productivity in the enterprise.
The global business trends involve Machine Learning, AI, end-to-end digitalization, sustainable development, Data Security, and EV with sales increasing by 85%
http://www.ericsson.com/thinkingahead/networked_society
Digitalization has unleashed a wave of transformation across a range of industries. The pace of change has been mind boggling and will only continue to accelerate. Everything from business models and product categories to financing and human resources will transform in order to take advantage of the possibilities of the Networked Society.
Sogeti on big data creating clarity - Report 1-4 on Big Data - Sogeti ViNTRick Bouter
Since 2005, when the term “Big Data” was launched, Big Data has become an increasingly topical theme. In terms of technological development and business adoption, the domain of Big Data has made powerful advances; and that is putting it mildly.
In this initial report on Big Data, the first of four, we give answers to questions concerning what exactly Big Data is, where it differs from existing data classification, how the transformative potential of Big Data can be estimated, and what the current situation (2012) is with regard to adoption and planning.
VINT attempts to create clarity in these developments by presenting experiences and visions in perspective: objectively and laced with examples. But not all answers, not by a long way, are readily available. Indeed, more questions will arise – about the roadmap, for example, that you wish to use for Big Data. Or about governance. Or about the way you may have to revamp your organization. About the privacy issues that Big Data raises, such as those involving social analytics. And about the structures that new algorithms and systems will probably bring us.
http://www.ict-books.com/books/inspiration-trends
Small data vs. Big data : back to the basicsAhmed Banafa
Small data is data in a volume and format that makes it accessible, informative and actionable.
The Small Data Group offers the following explanation:
Small data connects people with timely, meaningful insights (derived from big data and/or “local” sources), organized and packaged – often visually – to be accessible, understandable, and actionable for everyday tasks.
Since 2005, when the term “Big Data” was launched, Big Data has become an increasingly topical theme. In terms of technological development and business adoption, the domain of Big Data has made powerful advances; and that is putting it mildly.
In this initial report on Big Data, the first of four, we give answers to questions concerning what exactly Big Data is, where it differs from existing data classification, how the transformative potential of Big Data can be estimated, and what the current situation (2012) is with regard to adoption and planning.
VINT attempts to create clarity in these developments by presenting experiences and visions in perspective: objectively and laced with examples. But not all answers, not by a long way, are readily available. Indeed, more questions will arise – about the roadmap, for example, that you wish to use for Big Data. Or about governance. Or about the way you may have to revamp your organization. About the privacy issues that Big Data raises, such as those involving social analytics. And about the structures that new algorithms and systems will probably bring us.
http://www.ict-books.com/books/inspiration-trends
La base para optimizar y potenciar la toma de decisiones en cualqueir empresa es la información. Pero no la información en bruto, sino aquella de la que podemos obtener valor tras su análisis.
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A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
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2. Matteo Cristofaro General Management Course
2
Digital Darwinism;
Digital transformation;
What is Big Data?;
Sources;
Big Data and insights;
Ethics in Big Data;
Analyzing Big Data;
Culture-Staffing-Processes-Governance framework;
Analytics funcion;
References.
Summary
3. Digital Darwinism
3
We are now in the middle of the third industrial revolution, driven
by the omnipresent digitalization.
For this reason, it is again all about a fight for survival, which
companies successful for centuries such as mailorder houses and
publishing houses have lost their dominant positions.
It thus becomes apparent that it is no longer about size, it is not
necessarily about speed, and it is not only about the extent of
conforming or strength. Today, it is more about “The survival of
the smartest!” One should keep in mind what Charles Darwin
stated:
“It is not the strongest of the species that survives,nor the most intelligent
that survives.It is the one that is most adaptable to change”
Matteo Cristofaro General Management Course
4. 4
The most relevant developments could be characterized as the
DiSoLoMo-trend. This trend covers the following
dimensions: digital, social, local and mobile. In the first
instance, the challenges for companies lie in the trend to
digitalized more and more products and services (“digital”).
An additional trend is the increased use of social media.
Based on the trend of digitalisation more and more objects lose
significant constrations cause by their physical appearence
(e.g. books, newspaper, songs, etc.). This development is
called Zero Gravity thinking: objects lose the physical
restrictions they had in the real world when these objects are
transmitted into cyberspace.
Digital transformation (1) - Trends
Matteo Cristofaro General Management Course
6. Digital transformation (3) – Mobility
6
We are at the beginning of a development that is allowing the
smartphone to become a smart sevice terminal. This process is
renforced by the fact that more and more information of relevance
to purchasing processes is becoming available by mobile (by QR
code, RedLaserApp, etc.).
Mobility has eliminated the boundaries of space and time.
Customers are always connected, and companies can interact with
them at any time. The implications cannot be overstated. With
information about products becoming as important as the
products themselves, almost every company is now in the business
of creating and delivering “content” – information that is
personal, relevant and timely when accessed by the customer.
Partnership IBM-Apple http://www.apple.com/pr/library/2014/07/15Apple-and-IBM-Forge-Global-Partnership-to-
Transform-Enterprise-Mobility.html
Matteo Cristofaro General Management Course
7. 7
Therefore competition in many businesses is no longer based on mere product or services.
More and more companies undesrand that they have to create comprehensive and
integrated ecosystem in order to make the customer stay.
But in ecosystems, organizations realize value through the engagement with the
system as a whole, where “value” is defined by participants’ willingness to pay for access to
the ecosystem. Once access occurs, specific transactions may occur within the ecosystem.
Total value created reflects the value of access to and engagement within the system as a
whole.
Digital transformation (4) –
Integrated ecosystem
Matteo Cristofaro General Management Course
8. 8
Digital transformation (5) –
elements of digital transformation
What do businesses need to do to get ahead of the widespread forces for change in our digital age?
Key areas include reconfiguring the customer value proposition (what is being offered) and
reshaping the operating model (how it is delivered).
Matteo Cristofaro General Management Course
11. 11
Businesses aiming to generate new customer value propositions or transform their operating models
need to develop a new portfolio of capabilities for flexibility and responsiveness to fast-
changing customer requirements
Digital transformation (8) – A new portfolio
of capabilities
Matteo Cristofaro General Management Course
12. What is Big Data? (1)
12
The proliferation of digital products and services results in
consumers generating huge volumes of personal data across
all aspects of their lives, which is captured by organisations
through digital channels or devices.
Data about a person’s friendship networks, hobbies and
interests is captured on Facebook, while details of their
shopping patterns are captured by online retailers. In
addition to these relatively established data sources,
technological developments will continue to generate new sources of
consumer data that could offer unique insights into consumer
behaviour.
Matteo Cristofaro General Management Course
13. What is Big Data? (2)
13
Organizations are exploring how large-volume data can usefully
be deployed to create and capture value for individuals,
businesses, communities, and governments (McKinsey
Global Institute, 2011). big data is fast becoming a tool that
not only analyzes patterns, but can also provide the predictive
likelihood of an event.
The challenge for businesses lies in how to harness, make
sense of, and use this information to generate some form of
competitive advantage. Businesses that can access these
new sources of data, analyse them and convert them into
insight on consumer behaviour, will be able to make
their products and services more relevant to their customers.
Matteo Cristofaro General Management Course
14. What is Big Data? (Example)
14
Participant in a Formula 1 car race generates 20 gigabytes of data from
the 150 sensors on the car that can help analyze the technical performance
of its components, but also the driver reactions, pit stop delays, and
communication between crew and driver that contribute to overall
performance.
The emphasis thus moves away from outcomes (win/lose race), to instead
focus on each proximal, contributory element for success or failure
mapped for every second during the race.
Or…We could analyze the social networks and social engagement
behaviors of individuals by mapping mobility patterns onto physical
layouts of workspaces using sensors, or the frequency of meeting room
usage using remote sensors that track entry and exit patterns, which
could provide information on communication and coordination needs
based on project complexity and approaching deadlines.
Matteo Cristofaro General Management Course
15. Sources (1)
15
Big data is generated from an increasing plurality of sources:
Internet clicks, mobile transactions, user-generated content, and social media as
well as purposefully generated content through sensor networks or business
transactions such as sales queries and purchase transactions.
These data require the use of powerful computational techniques to unveil trends and
patterns within and between these extremely large socioeconomic datasets. “Big”
is no longer the defining parameter, but, rather, how “smart” it is—that is, the
insights that the volume of data can reasonably provide.
The defining parameter of big data is the fine-grained nature of the data itself,
thereby shifting the focus away from the number of participants to the granular
information about the individual.
Matteo Cristofaro General Management Course
16. Sources (2)
16
Big data is also a wrapper for different types of granular data. Below, we list five key
sources of high volume data:
Public data: held by governments, governmental organizations, and local
communities (e.g. energy use).
Private data: held by private firms, non-profit organizations, and individuals
(e.g. consumer transactions).
Data exhaust: ambient data that are passively collected, non-core data with
limited or zero value to the original data collection partner these data were
collected for a different purpose, but can be recombined with other data
sources to create new sources of value (e.g. Internet searches)
Community data: is a distillation of unstructured data—especially text into
dynamic networks that capture social trends (e.g.Twitter feeds).
Self quantification data: are revealed by the individual through quantifying
personal actions and behaviors (e.g. wristbands that monitor exercise and
movement).
Matteo Cristofaro General Management Course
18. Ethics in Big Data
18
Data sharing agreements need to be linked into the
mechanisms for data protection and privacy, including
anonymization for open data, access control, rights
management, and data usage control. Issues such as imputed
identity, where individual identity can be inferred through
data triangulation from multiple sources, will need to be
carefully considered and explicitly acknowledged and
permitted.
Matteo Cristofaro General Management Course
19. Analyzing Big Data(1)
19
The typical statistical approach of relying on “p”values to establish
the significance of a finding is unlikely to be effective because
the immense volume of data means that almost everything is
significant.
Using our typical statistical tools to analyze big data,it is very easy to
get false correlations.
However, this doesn’t necessarily mean that we should be
moving toward more and more complex and sophisticated
econometric techniques to deal with this problem; indeed,
such a response poses a substantial danger of over-fitting the
data.
Matteo Cristofaro General Management Course
20. Analyzing Big Data (2)
20
We use the term analytics as the process that extracts value from
data through creating and distributing reports, building and
deploying statistical and data-mining models, exploring and
visualizing data, sense-making, and other related techniques.
Data may be internal or external to the organization;
processing may be realtime, near real-time, or batch; and any
combination of these is possible.
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21. Analyzing Big Data (3)
21
There is a range of techniques that draw from several disciplines,
including statistics, computer science, applied mathematics, and
economics:
Cluster analysis;Data fusion and integration;Data mining;Genetic
algorithms; Machine learning;Natural language processing; Neural
networks;Network analysis; Signal processing;Spatial analysis;
Simulation;Time series analysis.
The challenge, though, is to shift away from focusing on p values to
focusing, rather, on effect sizes and variance explained. A pitfall of
big data-again, amplified by our commonly used statistical
techniques-lies in focusing too much on aggregates or
averages and too little on outliers.
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22. Analysing Big Data (4)
22
Once promising leads have been identified, the next challenge of
analyzing big data is to then move beyond identifying
correlational patterns to exploring causality.
Given the unstructured nature of most big data, causality is not built
into their design and the patterns observed are often open to a
wide range of possible causal explanations.
There are two main ways to approach this issue of causality.
1. Recognize the central importance of theory. An intuition about the
causal processes that generated the data can be used to guide the
development of theoretical arguments, grounded in prior
research and pushing beyond it.
2. A complementary way is to then test these theoretical
arguments in subsequent research-ideally, through field
experiments.
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Our organizational framework seeks to integrate analytics, business knowledge, and
information technology, and it is based on four main questions:
1. Does the organization view data and analytics as a key function of the organization?
2. Is there a critical mass of data scientists?
3. Are there data scientist with sufficiently deep knowledge of the business unit
domains?
4. Is there an adequate analytics governance structure?
Culture-Staffing-Processes-Governance
framework (1) - Integration
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The CsPg framework orients the organization designer to establishing a
culture for big data and analytics: hiring, training, and organizing a
group of analytics staff; developing the required analytics processes; and
setting up a robust analytics governance structure.
Starting with culture, corporate-level executives must recognize the need to
organize big data and analytics as an organizational function that is given
broad responsibility and authority for data assets and which is analogous
to other major functions in the organization.
Analytics staff must be able to obtain and manage data; build statistical,
predictive, and data-mining models; and deploy those models.
The more sophisticated the analytics processes become,the more opportunities that
can be pursued.
Culture-Staffing-Processes-Governance
framework (2) – Culture and Staff
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There are three basic models for locating the analytics function within the organization, all
of which involve well-known tradeoffs between centralization and decentralization.
1. One model centralizes analytics by placing the data scientists in a single unit.
When analytics is centralized, however, the data scientists may be far away from the
business units they are supposed to support. the challenge in such a structure is for the
data scientists to understand the various business units and their needs. In addition,
there is the issue of where the analytics department should report within the
organization.
2. One model decentralize analytics and place a group of data scientists in each
business unit. this approach makes it easier for data scientists to collaborate with their
respective business units and to tailor their models to each unit’s needs. the main
tradeoff is difficulty in achieving critical mass on enterprise-wide problems and
opportunities.
3. One model is a hybrid approach in which a critical mass of data scientists is housed
in a central unit, and the remaining data scientists are distributed
throughout the organization. One common hybrid model is to set up an analytics
or big data “center of excellence” that the distributed data scientists can draw on as
appropriate. another is to centralize the data scientists that interact with the IT
organization, or those that manage the data, or those that deploy the models.
Culture-Staffing-Processes-
Governance framework – 3 Models
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26. Analytics function (1) – 3 components
26
The analytics function is composed of 1) analytics models, 2) analytics
infrastructure, and 3) analytics operations.
1) A key analytics operation is building models, this is usually done by statisticians,
modelers, or, to use the new name, data scientists. In addition to building
models over data, analytics also includes summarizing data in reports (now
called descriptive analytics), ad hoc querying of data.
2) Analytics infrastructure refers to the software components, software services,
applications, and platforms for managing data, processing data, producing
models, and using models to generate alerts, take actions, and make decisions.
the key processes associated with analytics infrastructure are managing the data
required by the organization and deploying the models and other analytics that
are incorporated into the organization’s products, services, and operations.
3) Analytics operations refers to the various processes that result in the outputs of
models being used to make decisions and to take actions that bring business
value. Analytics operations ensures that the results of models are integrated into
an organization’s products, services, and operations.
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27. Analytics function (2) – Organizational
effects
27
An organization requires a critical mass of data scientists so that
their expertise as a whole extends across these three analytics
processes.The team as a whole must be able to:
Identify relevant data (both internal and external),manage the data required
for analytics,build the needed analytics models,and deploy the models that
are built into products,services,and internal systems.
Multiple parts of an organization can be involved in analytics
processes, typically, a business unit sponsors the model, an
analytics department builds the model, an IT unit supplies and
manages the data, and an operations unit deploys the model. With
so many diverse pieces of an organization involved, an analytics
governance structure is critical.
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Challenges for most organizations since the modeling group
must work with other components of the organization to
identify 1) analytics opportunities, 2) obtain the necessary data,
and 3) deploy the resulting models.
The role of an analytics governance structure is to put
in place an analytics leader with sufficient authority to
overcome these three challenges.
Analytics function (3) – Organizational
effects
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A complete set of parameters for designing a governance structure does
not exist. Grossman and Siegel (2014) suggest the following
preliminary parameters:
1. Ensure that sound long-term decisions about analytics are reached and
that investments in analytics generate business value.
2. Operate in such a way that data, derived data, and analytics products
are protected and managed in a secure and compliant fashion.
3. Operate in such a way as to make sure that there is accountability,
transparency, and traceability to those who are funding analytics
projects, to those who are developing and supporting analytics
resources, and to those who are making use of analytics resources.
4. Provide an organization structure to ensure that the necessary analytics
resources are available.
Analytics function (4) – Organizational
effects
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These design parameters can be achieved by using governance
committees:
an analytics governance committee that includes senior management and
representatives from the IT organization and various business stakeholders.
This committee helps prioritize analytics opportunities; obtain resources for
analytics projects; and ensure that those building the models get the data
required, that the models that are built get deployed, and that deployed
models measure the business value that they generate.
an analytics technical policy committee that determines what data, analytics
applications, processes, best practices, and standards are used across the
organization.
an analytics security and compliance committee that oversees the security and
compliance of data and analytics processes and applications.
an analytics data management and data quality committee that ensures the
organization’s data and metadata are accurate, complete, and consistent.
Analytics function (5) – Organizational
effects
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31. References
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George, G., Haas, M.R., Pentland, A.(2014). Big Data and Management.
Academy of Management Journal.Vol. 57, No. 2, Pp. 321–326.
Grossman, R.L., Siegel, K.P. (2014). Organizational Models for Big Data and
analytics. Journal of Organization Design.Vol. 3, Pp. 20-25.
E&Y UK. Digital data opportunities using insight to drive relevance in the
digital world. Web page: Advisory services, Performance improvement-Digital
transformation.
http://www.ey.com/UK/en/Services/Advisory/Performance-
Improvement/Advisory_Digital-Transformation . Accessed on 2nd October
2014.
Kreutzer, R.T., (2014). Digital Darwinism and the need for a a digital
transformation.4TH Annual conference on business strategy and Organizational
Behaviour.
IBM Global Business Services, (2011), Digital transformation:Creating new business
models where digital meets physical, IBM Institute for BusinessValue Executive
Report, Pp. 1-20.
IBM Global Business Services, (2013), Digital reinvention Preparing for a very
different tomorrow , IBM Institute for BusinessValue Executive Report, Pp. 1-24.
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